huggingface-trackio

To track ML experiment metrics in real time, log training values like loss and accuracy using a wandb-compatible API. The system stores data locally in SQLite or synchronizes it to Hugging Face Spaces, enabling real-time monitoring through local dashboards.

Yes, you can fire structured diagnostic alerts directly from your training code to flag issues like loss divergence, vanishing gradients, or training stalls. Alerts use severity levels including INFO, WARN, and ERROR to signal specific experiment problems.

Automate hyperparameter tuning by enabling LLM agents to poll for metrics and alerts via CLI. Agents retrieve diagnostic data to make data-driven decisions, allowing them to autonomously stop, adjust, or relaunch experiments for failure recovery.

Yes, trackio supports remote experiment tracking by synchronizing training metrics to Hugging Face Spaces. This allows you to access and monitor your machine learning runs remotely, serving as an alternative to strictly local SQLite storage.

The best way to enable autonomous iteration is combining diagnostic alerting with CLI metric retrieval. This allows automated agents to monitor long-running training runs, detect anomalies, and trigger autonomous experiment management without manual intervention.